Benchmark_mmp_digital_reputation_challenge_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Бенчмарк\n",
    "\n",
    "Для задачи **Digital Reputation Challenge**\n",
    "\n",
    "https://boosters.pro\n",
    "\n",
    "автор: Александр Дьяконов https://dyakonov.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 26) (462888, 2) (4000, 453) (4000, 6)\n"
     ]
    }
   ],
   "source": [
    "TRAIN_PATH = 'train/'\n",
    "X1 = pd.read_csv(TRAIN_PATH + 'X1.csv')\n",
    "X2 = pd.read_csv(TRAIN_PATH + 'X2.csv')\n",
    "X3 = pd.read_csv(TRAIN_PATH + 'X3.csv')\n",
    "Y = pd.read_csv(TRAIN_PATH + 'Y.csv')\n",
    "print (X1.shape, X2.shape, X3.shape, Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y.columns = ['Y' + s if s != 'id' else 'id' for s in Y.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4058, 26) (470083, 2) (4058, 453)\n"
     ]
    }
   ],
   "source": [
    "TEST_PATH = 'test/'\n",
    "X1_test = pd.read_csv(TEST_PATH + 'X1.csv')\n",
    "X2_test = pd.read_csv(TEST_PATH + 'X2.csv')\n",
    "X3_test = pd.read_csv(TEST_PATH + 'X3.csv')\n",
    "print (X1_test.shape, X2_test.shape, X3_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Подготовка обучения и теста\n",
    "\n",
    "используем только матрицу 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X1.copy()\n",
    "X = X.merge(Y)\n",
    "        \n",
    "id_ = X.pop('id')\n",
    "y1 = X.pop('Y1')\n",
    "y2 = X.pop('Y2')\n",
    "y3 = X.pop('Y3')\n",
    "y4 = X.pop('Y4')\n",
    "y5 = X.pop('Y5')\n",
    "\n",
    "X_test = X1_test.copy()\n",
    "id__ = X_test.pop('id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Эксперименты\n",
    "\n",
    "делаются так..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexander/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:390: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\tcv_agg's auc: 0.600372 + 0.0184634\n",
      "[200]\tcv_agg's auc: 0.597381 + 0.0157782\n",
      "[300]\tcv_agg's auc: 0.5931 + 0.0141725\n",
      "[400]\tcv_agg's auc: 0.590727 + 0.0117742\n",
      "[500]\tcv_agg's auc: 0.587088 + 0.0124422\n",
      "[600]\tcv_agg's auc: 0.586458 + 0.0104644\n",
      "[700]\tcv_agg's auc: 0.587516 + 0.00971511\n",
      "[800]\tcv_agg's auc: 0.587225 + 0.0104176\n",
      "[900]\tcv_agg's auc: 0.585616 + 0.0111163\n",
      "[1000]\tcv_agg's auc: 0.584474 + 0.0119534\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6026030903311532"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "param = {'n_estimators':1000, 'num_leaves':6, 'objective':'binary',\n",
    "         'learning_rate': 0.05, 'colsample_bytree': 0.75, 'subsample': 0.75,\n",
    "        'metric': 'auc'}\n",
    "         \n",
    "w = lgb.cv(param, lgb.Dataset(X, label=y1),\n",
    "           stratified=False,\n",
    "           num_boost_round=1000, nfold=4, verbose_eval=100)\n",
    "max(w['auc-mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ff9b9ddd8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uFqtUY2evGkKRQ/sPfNSmeRiDOrXk+z3pAJzKyWfii99y6NRZAPY/PdGf2VNK1QJb/VwudNjv8ZnVMbxra3YczeJ4Vi4DHlvmDgYAzy9N9mPOlFK1wVYBoaDIoesZVcLwrq0xBsb/45sy+/75VQrp2Xll0k/l5JN89HRdZE8pVcNs9e2Yk19EeHCgv7PRYPSLjQScax1dNbADG/4ylrUPjGHRb0cA8OXOsgvi/eHDzVz291X8sC+DPWnZ5OQXljlGKVU/2aoPIbegiPAQDQi+Cg0KZFiXVqzZm8GwLq3dM7zbNA+lfWQYS7Ye5drE4mWucguKWL7jGADXvvI9AJHhwSy9ZyRtI8LqvgBKqUqxVQ3hbH4RTTQgVMq7tw3jw18NZ+qgWHeaiPCzAe35cudxFm46TGGRg7zCIjYdPFXm/MyzBe4goZSq32wVEHLyiwjTJqNKEREGx7UqMzrr4gTnyrN3vbuBbg9+Tv9Hl7L7eDYAt4yIp2dMBIvvuojWTUPYeKBsoFBK1T+2azJq0UQXtqsJAzq1KLGdW+Dgw/WpBAUIsyb0IMjqvO8ZE0HyMe1kVqohsF0NQZuMakaTkCDm/WJwibRNB0/RsmmIOxgA9GjXnJ1HTrsXFlRK1V+2CghnC7TJqCaNOq8Nr9+cyNJ7RtLUCrQtmwSXOKZvbCT5RQ5+9s9vOZiR449sKqV8ZKuAUOQwuvR1DRvTsy3d2zZn48PjuHtMAk9O6Vti/xX92jOiWxTbj2Qx/fW17mdSKKXqH9sFhADRgFAbggMDuGdsdxLjWpVIDwwQ3rplCK9OH8T+9By+2HrUTzlUSnljq4DgMM5RM6puiQiX9mxLVLMQViSXncymlKofbBUQjDHoyhX+ERAgxEc1JfnoaX3GglL1lK2+Hh1Gm4z8qWOrJmw7nMWr3+z1d1aUUuWwVUDQPgT/+u1o59PYPlh30M85UUqVx1YBwRjQeOA/8VFNuWt0N/aeOMPKKvQlZJ4t4HRuQS3kTCkFNgsIDmMI1IjgVzcM60zbiFBmzFvHt7vP/eS11SknmP76Wr7fk87Oo1n0f3Qpl72wyr0/M6eAW+av44lF2zFG+yWUqi5bLV3hMPo8ZX9rGxHGby7pxsMLt3Hj62uZNaEHd1zctcxx6/ZnMGPeOvKLHHzjETgOZ+YSd/9njO/djiNZue4F9eZ+u49moUGs+MMlRDcPrbPyKNWY2KqGUGSMNhnVAzcN78xfrugFwFOf7+RAeskZzG+v/Ylr/v09+UUObrsovtxrfLHtaJnVVbPzCnnju321k2mlbMCngCAi40UkWURSROT+cvbPEJE0EdlovW712PeMiGy1Xj/3SI8XkbUisltE3heRWl91zugoo3pBRLhlRLz7y/7u9zcAsPHgKeLu/4wHP94KwAOX9+DBib1YM2sM82YMZvMj49j/9EQm9o1xX+udW4eSMnsCS+8ZSZ8OEby8cg/vrD1Q94VSqhHwGhBEJBCYA0wAegHTRKRXOYe+b4wZYL3mWudOBM4HBgBDgftEJMI6/hngBWNMAnASuKXapfHCYdA+hHpkpjXqaMOBU+w7cYYr53zn3vfq9EHcPtLZlNQuMoxRPdoQEeZcJ2nODedz1+hu3DGyCxd0iyIoMIDubZtz95juADzw8Rb3XIdTOfm8/u0+XVxPKR/40ocwBEgxxuwFEJH3gMnAdh/O7QV8bYwpBApFZBMwXkQ+BEYD11vHvQk8ArxcuexXjnMeQm1+gqqMyPBgVt03ipF/XcFDn2x1pzcNCWRY19bnPPfeceeVSRt1XjSR4cFkni2gywOLiWoWSoeW4Ww6eIq2EaGM6dFWn5in1Dn40mTUAfAcOJ5qpZU2VUQ2i8gCEXE9V3ETMEFEmohIFDAK6Ai0Bk5ZgeJc16wxxhhr2KlGhPoktmU4gLvjOGX2BJL+PNZdG6iMoMAAFt99kXv7RHaeu5/hb0t30fOhL5irk+KUqpAvAaG8b9DS9e9PgThjTD9gOc5f/BhjlgKLgdXAu8D3QKGP13R+uMjtIpIkIklpaWk+ZLd8rhYD7UOoXzxHfd0xsgtBgQHV+hXfoUU4j0/uDUBIUAAJbZrRrU0z9p44A8CHSanVy7BSjZgvTUapOH/Vu8QChz0PMMake2y+hrN/wLVvNjAbQETeAXYDJ4AWIhJk1RLKXNPj/FeBVwESExOr3BDssMap61pG9c9Hv7mA0KAAeraL8H6wD6YPj2P68Dj39ubUU0x6ydk/sT/9DHmFRYQGadORUqX58vW4DkiwRgWFANcBCz0PEJEYj81JwA4rPVBEWlvv+wH9gKXGOYtoBXC1dc7NwCfVKYg3roCgTUb1z/mdWtK7fWStzRHpF9uCe8d2Z2K/GPIKHUyZs5onFm0nr7CoVj5PqYbKaw3BGFMoIjOBJUAg8IYxZpuIPAYkGWMWAneJyCSczUEZwAzr9GDgG+tLOAu40aPf4E/AeyLyBLABeL3milVeOZx/apORPd01JoET2Xl8tvkI249ksf1IFos2H+Gxyb0Z17udv7OnVL3g00xlY8xinH0BnmkPebyfBcwq57xcnCONyrvmXpwjmOqEa9ihjjKyr6hmoTzys14s23GM9pHhfLLpMA98vIWxvdpqzVEpbLR0RXEfgv7Ht7MZF8Yz40LnhLg+HSJ5eOE2Xv92H7+4MF7/bSjbs00Xq2uUkf4SVC7jercF4InPdjD15dWcysn3c46U8i/bBATXapj6I1C5xESG8/iVfQDnshnXvbrGzzlSyr9sExCK+xA0Iqhi04d1Zs+TlwOw8+hpXeJC2ZptAsJ71lO6dPlrVVpggPDzROdUm42lVlBVyk5sExD+uiQZ0CYjVb7rhjgDwtSXV/s5J0r5j20Cgos2GanyJLRt7n7/m7fX8+mmw3z0oy5zoezFNsNOXbSGoMrTLDSIJ6f05YGPt7B4y1EWbzkKQGLnVnRq3cTPuVOqbmgNQSnL9UM7lUkb+dcV5OQXurf3nThD8tHTtlr2IuNMPu+sPUDS/gxblduObFhD0ICgKvbBHcPJziugTfMwPtl4iNe+2ceAR5dx28h4bh/ZlVHPrQRgYr8Y5lx/vn8zW0d+/8FGViQ7VxoOCQpg0W9HEBMZRvMqLFFeU4wxfLLxMP07tiA+qqnf8tHY2K+GYLsSq8oYEt+K0T3a0qdDJPdP6AlAfpGDOSv20P/Rpe7jPtt8xF9ZrFPGGLYeznJv5xc6GPfCKvo+spTvUk7w6KfbOHnGOaFv17HTxN3/GX9asJn07LxqfW7GmXwWrE8l40z5kwV3Hj3N797fyKjnVvL6t/oc7ZqiNQSlKhAYINw6Ip5Ch2H+6v3u9NE92vDVzuPsScuma3Qz/2WwFj37xU52Hj3N1PNjSTudx+wpfbh+SCf+tXKPe8TeDXPXAvDeDwcpdDgoKHLO4Xg/6SDvJx3k9ZsTuSghmpAg336FLd12lLDgQFo2CWHqv1eTX+ggtmU4i347ghZNih+5nltQxItf7nZvv73mJ24ZEe/e3nDgJA99so3AAGF8n3aMTIgmJjKM9DP5dGvTOO9XTRHXDN6GIDEx0SQlJVXp3Lj7PwPgn9MG8rP+7WsyW8oGjmSe5d21Bxjbqx1tI0IZ9dxKxvRsy68v6cq9H2wiIiyIwADh2av7Eduy/nZCnzyTT0R4sNd1m1z/XwBiIsP49LcjiGoWCjhrDW+t+Yll24+5n3TnMvX8WAyGj3485E7rGRPBvBmDaRcZVu5nnc4tICu3kAuf/gqAC7u1Zu3eDKYP78y87/bz6KTe3HxBHAAH0nO49IWvyS900C4ijOnDO/PXJcmsfWAMbSPCSM/OY9ATyyss154nL7flmlUist4Yk+jtOK0hKOWDmMjwEs9xnj48jn9/vYeFm0o+12nEMyt47/ZhDOty7mdC16X8QgcOY8gtKGLg48sY2KkFj0/uw4P/28rYnm2YOiiWzLMFfLH1KAM7tXQ/1hRgWJdWzL15MM1Ci78qRISbhsdx0/A41u3P4NY3k3j6qr5M6Fv8WJTdx7LZcigTgB1Hshj21Je8fetQTuUUMCS+FdHNncFl+utrywSV71LSmTKwAw9d0Yul247x8MJttI0I4+Lu0fzyzXXkFzoYGt+Kl64/n8OnzjoDwr4MJvVvX2L5kahmoZwo1XTV9YHFrHvwUvfnq5JsV0N4+YbzS/zDVaoqcvILGfP81xzJzC13/wd3DGdIfKs6zlVZ3+4+wY2vr63SuWtmjanwV703Gw+e4tNNhxl1XhsWbz3CO2sPuPdd2rMtc29O5Kudx/jlfOf/5x7tmnNtYkcOZOQwf/V+3rltKBd0jWLN3nT3l7zrC37mqG78flx3RITCIgfDnvqSM3lFLJx5IWNfWEXPmAg+L/Vs7UARBj6+DIBpQzoy+8q+tlq1QGsIFbDTPwJVe5qEBPHV7y/hWFYu7SLDKChycCqngIueXQHA51uP1HlA2H44iyXbjnLdkI7kFzo4mVNwzmDw7NR+/PG/m93bo3u0YUDHFuw6dpr+sS2qHAwABnRswYCOLQAYkRCFAB8kHaSgyLB8xzGeX5rM22sPEBoUwPJ7L6ZjK2czW25BETcN70wXq29mWJfWXNC1Nav3pHMiO49Xpg/iMo8HGgUFBvCri7vyxGc7GPvCKgBmTehRIi+upq6kP19K4hPLefeHg3SNbsatF3WpcvkaK1vUEIwxxM9yPt+nvlXnVeOy7XAmzy/dxYrk48y5/nwEarVG+sO+DK595XvevnUo/1qZwncp6WWOmTdjMGv3ZXA8K5fZU/qydPtR+sU6h2uezS9ic+ophtby/wmHw5Bf5GBl8nF+9Z8f3el/ntjT6xfz8axchjz5JRclRPHWLUPL7DfG8F1KOk99voOENs147pr+BFXw8PRdx05z9curKXQYNjw01jbP1va1hmCLgFBY5KDbg58DsOR3IzmvXXMvZyhVdSey8xg8e7n7sa23joind4cIpgyMrfHPuubfq1m3/2SF+3+e2JFnru5X459bVQ6Hof9jSwkNCuTuSxO4YUgnn2rtK5OP0y+2Ba2ahng91psvth5xB6XnrulP5tkCOrdqQs/2EUSEBfl1fkVt0YDgIbegiB5/+QLAPRpBqdo0/7t9/PvrvRzNKu5j2P/0RK/nGWPIOltIZJOyX0rHsnK5/B/fkH4mn74dItl5NMs91HN873Y0CQlkWNfWvPfDAa4c2IEJfWJoFhpEeEj9+hWcW1BEgIjPw1FrmjGGF79M4YXlu8rdf/Pwztw/oSeFDgfNQoMaxUO1NCB4yM4rpM/DSwDY+fh4woLr138Q1Xg98PGWEh2qAPN+MZhR57VhS2omf/rvZs7kFxIWFEibiFD3iJvmoUEs/O0I2rcI41ROAQ9+vJXlO46VuX7HVuF8fvfIEqOAlG8+WHeQTamnOJKZy9e70ogMDy4zEe6Oi7swy5qg2JBpQPCQmVNA/8eWcnH3aN785ZBayJlS5TPGsCctmylzVnM6r7DEvmFdWrFmb0aF594yIp4T2Xl8srF4aGt081BemjaQ1XvS+eWIeMKDA/32S7sx8vzx6DKxbwxPTe1LRANuStKA4OFEdh6JTyznscm9uWl4XM1nTCkfHM3MZfbiHXxaau5CdPNQ0k47x8u/OG0gyUez2H44y71+kMuc689nfJ92tpxYVZc2HTzF7z/cxH2Xnccdb613p//nlqGMSIjyY86qTgOCh2NZuQx98ktmT+nDDUM710LOlPLd1kOZzPpoC1sOZbrnxWw7nEmHFuHuJRoyzxZw3atryDpbwCczLyQ9O18HQ/iBaxSXy+7ZEwiuYARTfabzEDy4npMb2Ag6h1TD16dDJH+7tj8/HjjJ+D7OMfW920eWOCYyPLjE5CrXWHpVt4bEt2LDX8Yy/Y21bD2UxayPtjBzVDd+MX8dw7q05qmr+vo7izXKFgHBYdWCdFKaqi8S2jYv8ZQ2VX+1bBrCot9exJ3v/MiC9aksWO98kt6+E2eYdXkPzuQVEh4cSPMw72tE1Xc+BQQRGQ/8AwgE5hpjni61fwbwV8C1otVLxpi51r5ngYk4l9peBtxtjDEishKIAc5a54wzxhyvVmkq4HA4/9R1jJRSVfX8Nf1p2zyMd384wMi2ufg6AAAOgklEQVTuUXy54zj9HileEr110xC+/dNoRJytEk0b4MgvrzkWkUBgDjAWSAXWichCY8z2Uoe+b4yZWercC4ALAdfMmG+Bi4GV1vYNxpiqLU5UCUVWDaEBNv0ppeqJsOBAHvpZLx76WS8AFm46zF3vbnDvTz+TT8+HnPOd+ndswf9+c0GDm8PgSwgbAqQYY/YCiMh7wGSgdEAojwHCgBBAgGCg7GDqWuZuMmpgN0cpVX9N6t+eofGtaNEkmJDAAH71n/Us2eb8ett08BSPfrqd+yf0aFDznnwJCB2Agx7bqUDZBUVgqoiMBHYB9xhjDhpjvheRFcARnAHhJWPMDo9z5olIEfBf4AlTzpAnEbkduB2gU6eyz7z1hcOhAUEpVfM8Vz146frzyc4tpGloEIlPLGP+6v3MX72fQZ1bkp6dx/70HP5x3QAmD+jgxxyfmy+NKOV9i5b+4v4UiDPG9AOWA28CiEg3oCcQizOwjLaCBjibi/oCF1mv6eV9uDHmVWNMojEmMTo62ofsllXcZKQBQSlVO4IDA2jZNISQoAAWzhzB6B5tAFj/00n2p+cA8Jf/baWwyOHzNYschjOlJjTWJl8CQirQ0WM7Figxs8YYk26McT2J4jVgkPV+CrDGGJNtjMkGPgeGWeccsv48DbyDs2mqVhR3KtfWJyilVLG4qKa8MWMwyU+M56qBHbh3bHduuyierNxChjz5JUu3HfXpOk8u3kHvh5eQebaglnPs5EtAWAckiEi8iIQA1wELPQ8QEc/1fScBrmahA8DFIhIkIsE4O5R3WNtR1rnBwBXA1uoVpWLah6CU8ofQoED+9vMB3DUmgT9cdh7PX9Oflk2Cuf2t9Yz/+yp2HTtd4blFDsPr3+4DIPGJZec8tqZ47UMwxhSKyExgCc5hp28YY7aJyGNAkjFmIXCXiEwCCoEMYIZ1+gJgNLAFZzPTF8aYT0WkKbDECgaBOJuZXqvZohVzaJORUsrPQoMCmToolpjIMK6fu5adR08z7oVVXNEvht7tIzmTV8j+9DPcNDyOZduPsu+Es5lpysAOhAUHElONBxb5yhZLV2w4cJIp/1rNvBmDGWW16ymllL/8Z81PLNp8+JyLG0Lx3IbqLmGuS1d40JnKSqn65MZhnblxWGe2Hc5k0eYjFDkMm1NPcSI7n5Tj2Vw9KJY+7SOYcn5snT7PwiYBwfmnrmWklKpPerePLLOOlT/ZYu5ukXsegp8zopRS9ZgtAoJ7YppGBKWUqpA9AoKryUgDglJKVcgWAaHIaJORUkp5Y4uAoGsZKaWUd/YICDoxTSmlvLJFQCjSGoJSSnlli4CgaxkppZR3NgkIzj+1yUgppSpmi4CgE9OUUso7WwQEXctIKaW8s1dA0D4EpZSqkC0CguuJdbq4nVJKVcwWAaG4ycjPGVFKqXrMFl+ROlNZKaW8s0VAKNKZykop5ZUtAoJrHoJWEJRSqmK2CAhYNQRBI4JSSlXEFgHBqiDoxDSllDoHWwQEV6eyaJuRUkpVyBYBwVVD0HCglFIVs0dAsCKCDjtVSqmK+RQQRGS8iCSLSIqI3F/O/hkikiYiG63XrR77nhWRbSKyQ0ReFKvdRkQGicgW65ru9NrgmpimVQSllKqY14AgIoHAHGAC0AuYJiK9yjn0fWPMAOs11zr3AuBCoB/QBxgMXGwd/zJwO5BgvcZXsyxeaaeyUkpVzJcawhAgxRiz1xiTD7wHTPbx+gYIA0KAUCAYOCYiMUCEMeZ7Y4wB/g+4stK595GrhqCdykopVTFfAkIH4KDHdqqVVtpUEdksIgtEpCOAMeZ7YAVwxHotMcbssM5P9eGaNUJbjJRSyjtfAkJ536Om1PanQJwxph+wHHgTQES6AT2BWJxf+KNFZKSP18S6xu0ikiQiSWlpaT5kt+ILa6eyUkpVzJeAkAp09NiOBQ57HmCMSTfG5FmbrwGDrPdTgDXGmGxjTDbwOTDMumbsua7pce1XjTGJxpjE6OhoH7JbVnGTUZVOV0opW/AlIKwDEkQkXkRCgOuAhZ4HWH0CLpOAHdb7A8DFIhIkIsE4O5R3GGOOAKdFZJg1uugm4JNqlqVCpty6h1JKKU9B3g4wxhSKyExgCRAIvGGM2SYijwFJxpiFwF0iMgkoBDKAGdbpC4DRwBacLTdfGGM+tfb9GpgPhOOsOXxeU4WqiDYZKaVUxbwGBABjzGJgcam0hzzezwJmlXNeEXBHBddMwjkUtdYVL11RF5+mlFINkz1mKlt/ajxQSqmK2SMg6NIVSinllS0Cgo4yUkop72wRENxNRhoRlFKqQvYICMZo7UAppbywSUDQDmWllPLGHgEBox3KSinlhS0CgsNoh7JSSnlji4DgbDLSiKCUUudij4CAdiorpZQ39ggI2mSklFJe2SQgaKeyUkp5Y4uA4NBhp0op5ZUtAoKzyUhDglJKnYs9AoJ2KiullFf2CAjaZKSUUl7ZJCAYbTJSSikv7BEQgACNB0opdU62CAgOrSEopZRXtggI2oeglFLe2SMgoMNOlVLKG3sEBH1AjlJKeWWTgKCdykop5Y0tAoLDGF3+WimlvPApIIjIeBFJFpEUEbm/nP0zRCRNRDZar1ut9FEeaRtFJFdErrT2zReRfR77BtRs0YrpaqdKKeVdkLcDRCQQmAOMBVKBdSKy0BizvdSh7xtjZnomGGNWAAOs67QCUoClHofcZ4xZUI38+8Q5D0EjglJKnYsvNYQhQIoxZq8xJh94D5hchc+6GvjcGJNThXOrxWFMXX+kUko1OL4EhA7AQY/tVCuttKkisllEFohIx3L2Xwe8WypttnXOCyIS6luWq0CbjJRSyitfAkJ5X6Wlf3J/CsQZY/oBy4E3S1xAJAboCyzxSJ4F9AAGA62AP5X74SK3i0iSiCSlpaX5kN3yM6tNRkopdW6+BIRUwPMXfyxw2PMAY0y6MSbP2nwNGFTqGtcCHxtjCjzOOWKc8oB5OJumyjDGvGqMSTTGJEZHR/uQ3bIcOg9BKaW88iUgrAMSRCReREJwNv0s9DzAqgG4TAJ2lLrGNEo1F7nOEecU4iuBrZXLuu+c8xA0Iiil1Ll4HWVkjCkUkZk4m3sCgTeMMdtE5DEgyRizELhLRCYBhUAGMMN1vojE4axhfF3q0m+LSDTOJqmNwK+qXZoKOOchKKWUOhevAQHAGLMYWFwq7SGP97Nw9gmUd+5+yumENsaMrkxGq8OArm6nlFJe2GKmMtpkpJRSXtkiIGiTkVJKeWeLgKBLVyillHf2CAgYbTJSSikvbBEQHLpyhVJKeWWLgOBsMtIaglJKnYstAgIYfUCOUkp5YYuA4NBOZaWU8soWAcEY7VRWSilvfJqp3NAlxrUiO6/Q39lQSql6zRYB4c5R3fydBaWUqvds0WSklFLKOw0ISimlAA0ISimlLBoQlFJKARoQlFJKWTQgKKWUAjQgKKWUsmhAUEopBYAY03DWhhaRNOCnKp4eBZyowew0BFpme9Ay20N1ytzZGBPt7aAGFRCqQ0SSjDGJ/s5HXdIy24OW2R7qoszaZKSUUgrQgKCUUspip4Dwqr8z4AdaZnvQMttDrZfZNn0ISimlzs1ONQSllFLnYIuAICLjRSRZRFJE5H5/56cmiEhHEVkhIjtEZJuI3G2ltxKRZSKy2/qzpZUuIvKi9XewWUTO928Jqk5EAkVkg4gssrbjRWStVeb3RSTESg+1tlOs/XH+zHdViUgLEVkgIjut+z28sd9nEbnH+ne9VUTeFZGwxnafReQNETkuIls90ip9X0XkZuv43SJyc3Xy1OgDgogEAnOACUAvYJqI9PJvrmpEIfB7Y0xPYBhwp1Wu+4EvjTEJwJfWNjjLn2C9bgdervss15i7gR0e288AL1hlPgncYqXfApw0xnQDXrCOa4j+AXxhjOkB9MdZ9kZ7n0WkA3AXkGiM6QMEAtfR+O7zfGB8qbRK3VcRaQU8DAwFhgAPu4JIlRhjGvULGA4s8dieBczyd75qoZyfAGOBZCDGSosBkq33rwDTPI53H9eQXkCs9R9lNLAIEJyTdYJK329gCTDceh9kHSf+LkMlyxsB7Cud78Z8n4EOwEGglXXfFgGXNcb7DMQBW6t6X4FpwCse6SWOq+yr0dcQKP7H5ZJqpTUaVhV5ILAWaGuMOQJg/dnGOqyx/D38Hfgj4LC2WwOnjDGuh2Z7lstdZmt/pnV8Q9IFSAPmWc1kc0WkKY34PhtjDgHPAQeAIzjv23oa9312qex9rdH7bYeAIOWkNZqhVSLSDPgv8DtjTNa5Di0nrUH9PYjIFcBxY8x6z+RyDjU+7GsogoDzgZeNMQOBMxQ3I5SnwZfZavKYDMQD7YGmOJtMSmtM99mbispYo2W3Q0BIBTp6bMcCh/2UlxolIsE4g8HbxpiPrORjIhJj7Y8BjlvpjeHv4UJgkojsB97D2Wz0d6CFiARZx3iWy11ma38kkFGXGa4BqUCqMWattb0AZ4BozPf5UmCfMSbNGFMAfARcQOO+zy6Vva81er/tEBDWAQnWCIUQnJ1TC/2cp2oTEQFeB3YYY/7msWsh4BppcDPOvgVX+k3WaIVhQKaratpQGGNmGWNijTFxOO/jV8aYG4AVwNXWYaXL7Pq7uNo6vkH9cjTGHAUOish5VtIYYDuN+D7jbCoaJiJNrH/nrjI32vvsobL3dQkwTkRaWjWrcVZa1fi7U6WOOm4uB3YBe4AH/Z2fGirTCJxVw83ARut1Oc620y+B3dafrazjBedoqz3AFpwjOPxejmqU/xJgkfW+C/ADkAJ8CIRa6WHWdoq1v4u/813Fsg4Akqx7/T+gZWO/z8CjwE5gK/AWENrY7jPwLs4+kgKcv/Rvqcp9BX5plT0F+EV18qQzlZVSSgH2aDJSSinlAw0ISimlAA0ISimlLBoQlFJKARoQlFJKWTQgKKWUAjQgKKWUsmhAUEopBcD/AxrBTQho4PDRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(w['auc-mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Подготовка ответа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = 0.0\n",
    "for t in range(10):\n",
    "    model1 = lgb.LGBMClassifier(learning_rate=0.01, num_leaves=6,\n",
    "                                n_estimators=290,\n",
    "                                colsample_bytree=0.75, subsample=0.75, random_state=t)\n",
    "    model1.fit(X, y1)\n",
    "    a = model1.predict_proba(X_test)[:,1]\n",
    "    # print (a)\n",
    "    a1 += a\n",
    "a1 = a1 / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2 = 0.0\n",
    "for t in range(10):\n",
    "    model2 = lgb.LGBMClassifier(learning_rate=0.03, num_leaves=2,\n",
    "                          n_estimators=378,\n",
    "                          colsample_bytree=0.75, subsample=0.75, random_state=t)\n",
    "    model2.fit(X, y2)\n",
    "    a = model2.predict_proba(X_test)[:,1]\n",
    "    a2 += a\n",
    "a2 = a2 / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a3 = 0.0\n",
    "for t in range(10):\n",
    "    model3 = lgb.LGBMClassifier(learning_rate=0.01, num_leaves=4,\n",
    "                          n_estimators=543,\n",
    "                          colsample_bytree=0.75, subsample=0.75, random_state=t)\n",
    "    model3.fit(X, y3)\n",
    "    a = model3.predict_proba(X_test)[:,1]\n",
    "    a3 += a\n",
    "a3 = a3 / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "a4 = 0.0\n",
    "for t in range(10):\n",
    "    model4 = lgb.LGBMClassifier(learning_rate=0.003, num_leaves=6,\n",
    "                          n_estimators=618,\n",
    "                          colsample_bytree=0.75, subsample=0.75, random_state=t)\n",
    "    model4.fit(X, y4)\n",
    "    a = model4.predict_proba(X_test)[:,1]\n",
    "    a4 += a\n",
    "a4 = a4 / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "a5 = 0.0\n",
    "for t in range(10):\n",
    "    model5 = lgb.LGBMClassifier(learning_rate=0.002, num_leaves=3,\n",
    "                          n_estimators=516,\n",
    "                          colsample_bytree=0.75, subsample=0.75, random_state=t)\n",
    "    model5.fit(X, y5)\n",
    "    a = model5.predict_proba(X_test)[:,1]\n",
    "    #print (a)\n",
    "    a5 += a\n",
    "a5 = a5 / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.341597</td>\n",
       "      <td>0.336250</td>\n",
       "      <td>0.253492</td>\n",
       "      <td>0.300679</td>\n",
       "      <td>0.428683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.345812</td>\n",
       "      <td>0.227194</td>\n",
       "      <td>0.300929</td>\n",
       "      <td>0.282622</td>\n",
       "      <td>0.466563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.437719</td>\n",
       "      <td>0.484228</td>\n",
       "      <td>0.226082</td>\n",
       "      <td>0.307257</td>\n",
       "      <td>0.444477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.322215</td>\n",
       "      <td>0.274412</td>\n",
       "      <td>0.244045</td>\n",
       "      <td>0.301236</td>\n",
       "      <td>0.394336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>0.366907</td>\n",
       "      <td>0.253626</td>\n",
       "      <td>0.435525</td>\n",
       "      <td>0.380312</td>\n",
       "      <td>0.456793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id         1         2         3         4         5\n",
       "0   0  0.341597  0.336250  0.253492  0.300679  0.428683\n",
       "1   1  0.345812  0.227194  0.300929  0.282622  0.466563\n",
       "2   2  0.437719  0.484228  0.226082  0.307257  0.444477\n",
       "3   4  0.322215  0.274412  0.244045  0.301236  0.394336\n",
       "4   7  0.366907  0.253626  0.435525  0.380312  0.456793"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'id': X1_test.id.values,\n",
    "             '1': a1,\n",
    "             '2': a2,\n",
    "             '3': a3,\n",
    "             '4': a4,\n",
    "             '5': a5})\n",
    "df.to_csv('mmp_baseline_.csv', index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}